Development Of Computational Models Research Articles

Development of a three-dimensional computer model of the equine heart using a polyurethane casting technique and in vivo contrast-enhanced computed tomography

Introduction/objectivesInsight into the three-dimensional (3D) anatomy of the equine heart is essential in veterinary education and to develop minimally invasive intracardiac procedures. The aim was to create a 3D computer model simulating the in vivo anatomy of the adult equine heart. AnimalsTen horses and five ponies. Materials and methodsTen horses, euthanized for non-cardiovascular reasons, were used for in situ cardiac casting with polyurethane foam and subsequent computed tomography (CT) of the excised heart. In five anaesthetized ponies, a contrast-enhanced electrocardiogram-gated CT protocol was optimized to image the entire heart. Dedicated image processing software was used to create 3D models of all CT scans derived from both methods. Resulting models were compared regarding relative proportions, detail and ease of segmentation. ResultsThe casting protocol produced high detail, but compliant structures such as the pulmonary trunk were disproportionally expanded by the foam. Optimization of the contrast-enhanced CT protocol, especially adding a delayed phase for visualization of the cardiac veins, resulted in sufficiently detailed CT images to create an anatomically correct 3D model of the pony heart. Rescaling was needed to obtain a horse-sized model. ConclusionsThree-dimensional computer models based on contrast-enhanced CT images appeared superior to those based on casted hearts to represent the in vivo situation and are preferred to obtain an anatomically correct heart model useful for education, client communication and research purposes. Scaling was, however, necessary to obtain an approximation of an adult horse heart as cardiac CT imaging is restricted by thoracic size.

Strain-rate-dependent dynamic compression–shear response of alumina

This research article investigated the stress-state and strain-rate-dependent mechanical properties and failure of alumina ceramic. Compression–shear testing was carried out using a split-Hopkinson pressure bar under equivalent strain rates ranging from 101 s−1 to 103 s−1. Ultra-high-speed images coupled with digital image correlation were used to investigate the in situ time-evolving strain field and the failure process (e.g., crack growth). Experimental results showed equivalent strength reduced from 4.27 ± 0.23 GPa for the uniaxial compression tests to 3.75 GPa and 2.00 GPa for compression–shear tests across the strain rates and shear angles (5° to 25°), with the strength decreasing with an increasing shear angle. The crack speed ranged from 908 m/s to 3208 m/s across the range of strain rates and shear angles, with a positive correlation noted for crack speeds with strain rate and increasing sensitivity with an increasing shear angle. The extent of localization of shear strain in alumina under compression–shear deformation was observed to be strain-rate-dependent, and it increased with an increasing strain rate of deformation. Furthermore, axial and shear strain localization increased with the increasing shear angle, contributing to local shear failure before the complete failure of the specimen. Overall, this paper provides new insights and measurements of the strain-rate-dependent and shear-dependent strength and crack speeds of alumina under compression–shear loading, and these serve as vital inputs for future computational model development and validation.

Actual Shear Rate Prediction Associated with Wall Slip Phenomenon Using Radial Basis Function Network

Wall slip can be defined as a phenomenon where the particles in a suspension moving away from the wall boundary, leaving a thin liquid-rich layer adjacent to the boundary. Such a phenomenon may induce a significant impact on rheological measurements, particularly to viscosity, shear stress, and shear rate. Suspension has wide applications such as food processing, personal care products, pharmaceuticals, paints, medicine, and agrochemical. The traditional technique for the actual shear rate prediction is challenging yet difficult and non-favourable from the perspective of time and cost-effectiveness. Therefore, development of a mathematical computational model which has the ability to perform the prediction task with an acceptable level of accuracy is highly in need. Since radial basis function network (RBFN) has the ability to perform input-output mapping in a highly accurate manner, both of the approaches are employed to generate the actual shear rate prediction model. Through the model evaluation using a series of statistical analyses, it was found that RBFN model V is the best model as it shows the highest coefficient of determination (0.9998), lowest mean squared error (0.001058) and root mean squared error (0.001447), most negative Akaike information criterion (-18426.5) and Bayesian information criterion (-18415.5), and the smallest percentage error.

Biophysical Principles Emerging from Experiments on Protein-Protein Association and Aggregation.

Protein-protein association and aggregation are fundamental processes that play critical roles in various biological phenomena, from cellular signaling to disease progression. Understanding the underlying biophysical principles governing these processes is crucial for elucidating their mechanisms and developing strategies for therapeutic intervention. In this review, we provide an overview of recent experimental studies focused on protein-protein association and aggregation. We explore the key biophysical factors that influence these processes, including protein structure, conformational dynamics, and intermolecular interactions. We discuss the effects of environmental conditions such as temperature, pH and related buffer-specific effects, and ionic strength and related ion-specific effects on protein aggregation. The effects of polymer crowders and sugars are also addressed. We list the techniques used to study aggregation. We analyze emerging trends and challenges in the field, including the development of computational models and the integration of multidisciplinary approaches for a comprehensive understanding of protein-protein association and aggregation.

MATHEMATICAL MODEL OF THE UAV MOTOR FOR REAL TIME SIMULATION

The paper describes the development of a fast computational mathematical model of a UAV propeller installation for flight simulation in real time

Precipitation forecasting: from geophysical aspects to machine learning applications

Intense precipitation events pose a significant threat to human life. Mathematical and computational models have been developed to simulate atmospheric dynamics to predict and understand these climates and weather events. However, recent advancements in artificial intelligence (AI) algorithms, particularly in machine learning (ML) techniques, coupled with increasing computer processing power and meteorological data availability, have enabled the development of more cost-effective and robust computational models that are capable of predicting precipitation types and aiding decision-making to mitigate damage. In this paper, we provide a comprehensive overview of the state-of-the-art in predicting precipitation events, addressing issues and foundations, physical origins of rainfall, potential use of AI as a predictive tool for forecasting, and computational challenges in this area of research. Through this review, we aim to contribute to a deeper understanding of precipitation formation and forecasting aided by ML algorithms.

Development and Validation of Nonhuman Primate Head-Neck Computational Model for Frontal Impact Injury Analysis

Abstract Severe cervical spine injuries are rare in an automobile crash, however, the recovery for an individual is difficult. With no suitable surrogate in the laboratory setting, the exact head-neck (HN) response to severe impact accelerations is unknown. Therefore, this study aimed to develop a nonhuman primate (NHP) HN model and validate it using a historic NHP kinematic dataset that tested noninjury, as well as injury-inducing impact accelerations. The geometry of the NHP HN model was constructed from a previously CT-scanned skeleton and idealized as a two-dimensional quadrilateral shell mesh. Inertial properties of the vertebra and skull were defined, as well as 1D beam elements representing the spinal ligaments and discs. The model was then driven at the T1 vertebra using a literature-derived 10G acceleration curve to simulate frontal impact. Output peak Head X-acceleration was measured at 19.8G, which fell within the average peak response of 18.8 ± 4.6 G. Capsular ligament and interspinous ligament strains were measured along the cervical spine and the relative magnitudes were consistent with areas of likely injury at more severe impact accelerations. Once tested at more severe impact accelerations, this NHP HN model will provide a suitable way to study potential human cervical spine dynamics during frontal impact.

FRI478 Pharmacometrics-based Computer Model Utilizing Heart Rate To Monitor Thyroid Function In Children With Graves' Disease

Abstract Disclosure: B. Steffens: None. G. Koch: None. F. Claude: None. F. Bachmann: None. J. Schropp: None. M. Janner: None. D. l'Allemand: None. D. Konrad: None. M. Pfister: None. G. Szinnai: None. Graves’ disease (GD) with onset in childhood or adolescence is a pediatric rare disease (ORPHA:525731) with a ten times lower incidence than in adults. Leading clinical signs of hyperthyroidism are sinus tachycardia, weight loss, tremor, and goiter. First-line treatment are anti-thyroid drugs in order to normalize thyroid function. However, dose finding in pediatric GD is complex due to a broad spectrum of disease severity at diagnosis, and highly variable disease activity during follow-up, especially during puberty. As thyroid hormones have a strong positive chronotropic effect, heart rate (HR) turns out to be a useful clinical marker to monitor thyroid activity under treatment. Our overall aim was to provide a practical pharmacometrics-based (PMX-based) computer model characterizing both, individual FT4 dynamics and the relation between FT4 and tachycardia in children with various disease severity of GD during the first 120 days of treatment. Development of the PMX computer model was based on the non-linear mixed effects approach (i) linking FT4 kinetics with HR dynamics, (ii) accounting for inter-individual variability, and (iii) incorporating individual patient characteristics. Retrospectively collected clinical (resting heart rate during consultation) and laboratory data (FT4) from 41 children and adolescents with GD at four pediatric hospitals in Switzerland (75% female, median age 11.2 [IQR 8.5, 13.5] years) with 187 FT4 measurements and 132 HR measurements, and 124 paired measurements, were available and used for model development. Pediatric patients showed median FT4 of 59.6 [IQR 44.5, 73.0] pmol/l and median HR of 112 [IQR 100, 128] bpm at diagnosis. GD severity groups were defined based on FT4 measurement at diagnosis, resulting in equal numbers of mild (13), moderate (14) and severe (14) GD. We observed a significant difference in HR at diagnosis (p < 0.01) between the three GD severity groups based on FT4 at diagnosis. The final PMX computer model accounted for inter-individual variability and clinically relevant covariate effects such as age, gender, and GD severity, and was able to accurately predict FT4 and HR dynamics for each individual patient during the first 120 days of treatment. A PMX-based computer model that leverages individual HR dynamics can be applied to facilitate personalized pharmacotherapy in pediatric GD and mitigate the risk for under- or overdosing of anti-thyroid drugs in these patients. Prospective randomized validation trials are warranted to further validate and fine-tune such computer-supported personalized dosing in children with Graves’ disease. Presentation: Friday, June 16, 2023

Machine learning-based prediction and mathematical optimization of Capecitabine solubility through the supercritical CO2 system

This work represents development of computational intelligence models for assessment of estimating solubility/bioavailability of orally-taken drugs in SCF (supercritical fluid). The SCF considered in this work is CO2. This approach can provide an appropriate opportunity for the development of sustainable and environmentally-friendly technology with valuable advantages in the drug delivery industry. We modeled the solubility of Capecitabine in CO2SCF using multiple machine learning models. The inputs are Pressure and Temperature in the used dataset and the single output (Y) is the solubility of the drug. Three well-known supervised learning models of KNN (K-Nearest Neighbor), MLP (Multilayer Perceptron), and SVR (Support Vector Regression) were selected for modeling in this research. The hyper-parameters of them were optimized using Cuckoo search algorithm (CS) and then final models assessed using multiple metrics. In terms of coefficient of determination, KNN, MLP, and SVR models have score values of 0.959, 0.996, and 0.952, respectively. Also, they have error rates of 13.78, 1.54, and 19.23 with MSE metric. These facts introduce MLP as the best model obviously and so we used this model for final analysis in this study due to its superior architecture.

Initial interactions with the FDA on developing a validation dataset as a medical device development tool.

Quantifying tumor-infiltrating lymphocytes (TILs) in breast cancer tumors is a challenging task for pathologists. With the advent of whole slide imaging that digitizes glass slides, it is possible to apply computational models to quantify TILs for pathologists. Development of computational models requires significant time, expertise, consensus, and investment. To reduce this burden, we are preparing a dataset for developers to validate their models and a proposal to the Medical Device Development Tool (MDDT) program in the Center for Devices and Radiological Health of the U.S. Food and Drug Administration (FDA). If the FDA qualifies the dataset for its submitted context of use, model developers can use it in a regulatory submission within the qualified context of use without additional documentation. Our dataset aims at reducing the regulatory burden placed on developers of models that estimate the density of TILs and will allow head-to-head comparison of multiple computational models on the same data. In this paper, we discuss the MDDT preparation and submission process, including the feedback we received from our initial interactions with the FDA and propose how a qualified MDDT validation dataset could be a mechanism for open, fair, and consistent measures of computational model performance. Our experiences will help the community understand what the FDA considers relevant and appropriate (from the perspective of the submitter), at the early stages of the MDDT submission process, for validating stromal TIL density estimation models and other potential computational models. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Scope and limitations of ad hoc neural network reconstructions of solar wind parameters

Context.Solar wind properties are determined by the conditions of their solar source region and transport history. Solar wind parameters, such as proton speed, proton density, proton temperature, magnetic field strength, and the charge state composition of oxygen, are used as proxies to investigate the solar source region of the solar wind. The solar source region of the solar wind is relevant to both the interaction of this latter with the Earth’s magnetosphere and to our understanding of the underlying plasma processes, but the effect of the transport history of the wind is also important. The transport and conditions in the solar source region affect several solar wind parameters simultaneously. Therefore, the typically considered solar wind properties (e.g., proton density and oxygen charge-state composition) carry redundant information. Here, we are interested in exploring this redundancy.Aims.The observed redundancy could be caused by a set of hidden variables that determine the solar wind properties. We test this assumption by determining how well a (arbitrary, non-linear) function of four of the selected solar wind parameters can model the fifth solar wind parameter. If such a function provided a perfect model, then this solar wind parameter would be uniquely determined from hidden variables of the other four parameters and would therefore be redundant. If no reconstruction were possible, this parameter would be likely to contain information unique to the parameters evaluated here. In addition, isolating redundant or unique information contained in these properties guides requirements for in situ measurements and development of computer models. Sufficiently accurate measurements are necessary to understand the solar wind and its origin, to meaningfully classify solar wind types, and to predict space weather effects.Methods.We employed a neural network as a function approximator to model unknown, arbitrary, non-linear relations between the considered solar wind parameters. This approach is not designed to reconstruct the temporal structure of the observations. Instead a time-stable model is assumed and each point of measurement is treated separately. This approach is applied to solar wind data from the Advanced Composition Explorer (ACE). The neural network reconstructions are evaluated in comparison to observations, and the resulting reconstruction accuracies for each reconstructed solar wind parameter are compared while differentiating between different solar wind conditions (i.e., different solar wind types) and between different phases in the solar activity cycle. Therein, solar wind types are identified according to two solar-wind classification schemes based on proton plasma properties.Results.Within the limits defined by the measurement uncertainties, the proton density and proton temperature can be reconstructed well. Each parameter was evaluated with multiple criteria. Overall proton speed was the parameter with the most accurate reconstruction, while the oxygen charge-state ratio and magnetic field strength were most difficult to recover. We also analysed the results for different solar wind types separately and found that the reconstruction is most difficult for solar wind streams preceding and following stream interfaces.Conclusions.For all considered solar wind parameters, but in particular the proton density, proton temperature, and the oxygen charge-state ratio, parameter reconstruction is hindered by measurement uncertainties. The proton speed, while being one of the easiest to measure, also seems to carry the highest degree of redundancy with the combination of the four other solar wind parameters. Nevertheless, the reconstruction accuracy for the proton speed is limited by the large measurement uncertainties on the respective input parameters. The reconstruction accuracy of sector reversal plasma is noticeably lower than that of streamer belt or coronal hole plasma. We suspect that this is a result of the effect of stream interaction regions, which strongly influence the proton plasma properties and are typically assigned to sector reversal plasma. The fact that the oxygen charge-state ratio –a non-transport-affected property– is difficult to reconstruct may imply that recovering source-specific information from the transport-affected proton plasma properties is challenging. This underlines the importance of measuring the heavy ion charge-state composition.

Numerical simulation of hydraulic fracture extension patterns at the interface of coal-measure composite rock mass with Cohesive Zone Model

This study presents the development of a numerical computational model for hydraulic fracturing of coal-measure rock mass. The model is based on the B-K hybrid fracture energy criterion and is implemented using the extended finite element method (XFEM) and cohesive zone model (CZM) in the ABAQUS software. The study took into account the elastic-plastic damage properties of the coal-measure rock mass, as well as the combined effects of tensile and shear damage during the start and expansion of hydraulic fractures. The expansion trajectories and fracture morphology of hydraulic fractures at the interface of coal-measure rock mass under different lithological combinations (lithological strength differences) and ground stress conditions are revealed, and the main controlling factors affecting the expansion across the bedding are clarified. The study provides valuable insights into the management of hydraulic fracturing in the challenging context of hard roof coal mines, the stimulation of coal-measure gas co-production reservoirs, and the development of effective fracturing stimulation strategies for shale oil reservoirs interlayered with thin sandstone, and it can promote the development of industrial production technology for coal-measure gas and shale oil and gas resources, and is beneficial to increase the proportion of clean energy in China's energy consumption structure, and thus promoting a further reduction in the carbon emissions from energy consumption.

Deformations caused by subsurface heat islands: a study on the Chicago Loop

Deformations caused by subsurface heat islands: a study on the Chicago Loop

Machine learning accelerated design of auxetic structures

Auxetic metamaterials exhibit unusual expansion perpendicular to the direction of tensile loading, a behavior known as negative Poisson's ratio (NPR). The microstructures of auxetic metamaterials require rational design. It is a significant challenge when optimizing auxetic cellular structures solely through finite element calculations (FECs) due to the vast number of possibilities. In present work, two machine learning (ML), Evolutionary Computation (EC) and Artificial Neural Network (ANN), are employed to accelerate the design and optimization process. During ML training, auxetic cellular structures are encoded using binary sequences consisting only of 0s and 1s. FECs are conducted on some auxetic cellular structures to generate initial data, which are then used to train the EC and ANN models for efficient prediction of the optimal auxetic structure. In addition, the ANN model demonstrates a search speed at least five orders of magnitude faster than FECs when exploring the structure space, outperforming EC in the present task. Finally, the optimal auxetic structures are manufactured by using 3D printing. Their effective Poisson's ratios closely match those obtained from FECs. The present work showcases the powerful capabilities of ML in the design of metamaterials.

Multiple neuron clusters on Micro-Electrode Arrays as an in vitro model of brain network

Understanding the brain functioning is essential for governing brain processes with the aim of managing pathological network dysfunctions. Due to the morphological and biochemical complexity of the central nervous system, the development of general models with predictive power must start from in vitro brain network engineering. In the present work, we realized a micro-electrode array (MEA)-based in vitro brain network and studied its emerging dynamical properties. We obtained four-neuron-clusters (4N) assemblies by plating rat embryo cortical neurons on 60-electrode MEA with cross-shaped polymeric masks and compared the emerging dynamics with those of sister single networks (1N). Both 1N and 4N assemblies exhibited spontaneous electrical activity characterized by spiking and bursting signals up to global activation by means of network bursts. Data revealed distinct patterns of network activity with differences between 1 and 4N. Rhythmic network bursts and dominant initiator clusters suggested pacemaker activities in both assembly types, but the propagation of activation sequences was statistically influenced by the assembly topology. We proved that this rhythmic activity was ivabradine sensitive, suggesting the involvement of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, and propagated across the real clusters of 4N, or corresponding virtual clusters of 1N, with dominant initiator clusters, and nonrandom cluster activation sequences. The occurrence of nonrandom series of identical activation sequences in 4N revealed processes possibly ascribable to neuroplasticity. Hence, our multi-network dissociated cortical assemblies suggest the relevance of pacemaker neurons as essential elements for generating brain network electrophysiological patterns; indeed, such evidence should be considered in the development of computational models for envisaging network behavior both in physiological and pathological conditions.

Computational design of catalysts for ammonia synthesis

Ammonia plays a crucial role in agriculture and chemical engineering, and acts as a promising carbon-free transportation fuel. Catalysts design is deemed as a key to solve the restriction of energy-intensive Haber–Bosch process of ammonia production. With the development of computational modeling, computation-aided catalyst design serves as one important driving force for material innovation, saving a lot of experimental efforts based on trial and error. Computational modeling not only provides fundamental mechanistic insights into the reaction with great details regarding adsorbate geometries, electronic structures, and elementary-step energies, but also expedites the material discovery with descriptor-based catalyst design, core of which is the establishment of thermo/kinetic scaling relations. In this review, we present firstly the mechanistic understanding of ammonia synthesis and transition state scaling relations developed on pure transition-metal catalysts. We then summarize catalysts design strategies guided by alloy, size, and magnetic effects with the goal of breaking the limitations set by scaling relations to achieve better catalytic performance. Finally, future opportunities and challenges associated with computation design of optimal catalysts for ammonia synthesis are outlined.

A co-rotational formulation for quasi-steady aerodynamic nonlinear analysis of frame structures

The design of structures submitted to aerodynamic loads usually requires the development of specific computational models considering fluid-structure interactions. Models using structural frame elements are developed in several relevant applications such as, the design of advanced aircraft wings, wind turbine blades or power transmission lines. In the case of flexible frame structures submitted to fluid flows, the computation of inertial and aerodynamic forces for large displacements and rotations is a challenging task. In this article, we present a novel formulation for the efficient computation of aerodynamic forces in frame structures, coupling the co-rotational framework with the quasi-steady theory. A numerical procedure is provided considering a tangent matrix for the aerodynamic forces. This formulation is implemented in the open-source library ONSAS, allowing users to reproduce the results or solve other frame nonlinear dynamic problems. The proposed formulation and its implementation are validated through the resolution of four numerical examples. The formulation and the numerical procedure proposed efficiently provide accurate solutions for these challenging problems with large displacements and rotations.

Paint and Coating Physics

Paints and Coatings are ubiquitous with wide ranging applications in architectural and construction, aerospace, automotive, electronic, food, and the pharmaceutical industries. The manufacture and storage of paints, their application on a substrate, and the film formation process all involve fluid flow whose understanding and control is important for achieving the desired finish. Within this context, this special issue presents developments in advanced computational models, experiments, and analysis related to the various stages of paint formulation and their applications.

Gas Barrier Properties of Multilayer Polymer–Clay Nanocomposite Films: A Multiscale Simulation Approach

The paper discusses the development of a multiscale computational model for predicting the permeability of multilayer protective films consisting of multiple polymeric and hybrid layers containing clay minerals as fillers. The presented approach combines three levels of computation: continuous, full atomic, and quantitative structure–property correlations (QSPR). Oxygen and water are chosen as penetrant molecules. The main predictions are made using the continuum model, which takes into account the real scales of films and nanoparticles. It is shown that reliable predictions of the permeability coefficients can be obtained for oxygen molecules, which is not always possible for water. The latter requires the refinement of existing QSPR methods and interatomic interaction potentials for the atomistic level of calculations. Nevertheless, we show that the maximum effect on permeability reduction from the addition of clay fillers to the hybrid layer can be achieved by using nanoparticles with large aspect ratios and a high degree of orientational order. In addition, the use of the hybrid layer should be combined with the use of polymer layers with minimal oxygen and water permeability. The constructed model can be used to improve the properties of protective coatings for food and drug storage and to regulate the gas permeability of polymeric materials.

Gas Microfilms in Droplet Dynamics: When Do Drops Bounce?

In the last ten years, advances in experimental techniques have enabled remarkable discoveries of how the dynamics of thin gas films can profoundly influence the behavior of liquid droplets. Drops impacting onto solids can skate on a film of air so that they bounce off solids. For drop–drop collisions, this effect, which prevents coalescence, has been long recognized. Notably, the precise physical mechanisms governing these phenomena have been a topic of intense debate, leading to a synergistic interplay of experimental, theoretical, and computational approaches. This review attempts to synthesize our knowledge of when and how drops bounce, with a focus on ( a) the unconventional microscale and nanoscale physics required to predict transitions to/from merging and ( b) the development of computational models. This naturally leads to the exploration of an array of other topics, such as the Leidenfrost effect and dynamic wetting, in which gas films also play a prominent role.